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Cluster Metrics and Temporal Coherency in Pixel Based Matrices

Abstract In this thesis, the application of pixel-based vertical axes used within parallel coordinate plots is explored in an attempt to improve how existing tools can explain complex multivariate interactions across temporal data. Several promising visualization techniques are combined, such as: visual boosting to allow for quicker consumption of large data sets, the bond energy algorithm to find finer patterns and anomalies through contrast, multi-dimensional scaling, flow lines, user guided clustering, and row-column ordering. User input is applied on precomputed data sets to provide for real time interaction. General applicability of the techniques are tested against industrial trade, social networking, financial, and sparse data sets of varying... (more)
Created Date 2014
Contributor Hayden, Thomas (Author) / Maciejewski, Ross (Advisor) / Wang, Yalin (Committee member) / Runger, George (Committee member) / Mack, Elizabeth (Committee member) / Arizona State University (Publisher)
Subject Computer science / Data / Industry / Media / Social / Visualization
Type Masters Thesis
Extent 69 pages
Language English
Reuse Permissions All Rights Reserved
Note M.S. Computer Science 2014
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis